code: def main():
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.
  
      parser = HfArgumentParser(
          (ModelArguments, DataTrainingArguments, TrainingArguments)
      )
      if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          model_args, data_args, training_args = parser.parse_json_file(
              json_file=os.path.abspath(sys.argv[1])
          )
      else:
          (
              model_args,
              data_args,
              training_args,
          ) = parser.parse_args_into_dataclasses()
  
      # Detecting last checkpoint.
      last_checkpoint = None
      if (
          os.path.isdir(training_args.output_dir)
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
          last_checkpoint = get_last_checkpoint(
              training_args.output_dir
          )
          if (
              last_checkpoint is None
              and len(os.listdir(training_args.output_dir)) > 0
          ):
              raise ValueError(
                  f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                  "Use --overwrite_output_dir to overcome."
              )
          elif last_checkpoint is not None:
              logger.info(
                  f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                  "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
              )
  
      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          handlers=[logging.StreamHandler(sys.stdout)],
      )
      logger.setLevel(
          logging.INFO
          if is_main_process(training_args.local_rank)
          else logging.WARN
      )
  
      # Log on each process the small summary:
      logger.warning(
          f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
          + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
      )
      # Set the verbosity to info of the Transformers logger (on main process only):
      if is_main_process(training_args.local_rank):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
      logger.info("Training/evaluation parameters %s", training_args)
  
      # Set seed before initializing model.
      set_seed(training_args.seed)
  
      # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
      # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
      # (the dataset will be downloaded automatically from the datasets Hub).
      #
      # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
      # 'text' is found. You can easily tweak this behavior (see below).
      #
      # In distributed training, the load_dataset function guarantee that only one local process can concurrently
      # download the dataset.
      if data_args.dataset_name is not None:
          # Downloading and loading a dataset from the hub.
          datasets = load_dataset(
              data_args.dataset_name, data_args.dataset_config_name
          )
          if "validation" not in datasets.keys():
              datasets["validation"] = load_dataset(
                  data_args.dataset_name,
                  data_args.dataset_config_name,
                  split=f"train[:{data_args.validation_split_percentage}%]",
              )
              datasets["train"] = load_dataset(
                  data_args.dataset_name,
                  data_args.dataset_config_name,
                  split=f"train[{data_args.validation_split_percentage}%:]",
              )
      else:
          data_files = {}
          if data_args.train_file is not None:
              data_files["train"] = data_args.train_file
          if data_args.validation_file is not None:
              data_files["validation"] = data_args.validation_file
          extension = data_args.train_file.split(".")[-1]
          if extension == "txt":
              extension = "text"
          datasets = load_dataset(extension, data_files=data_files)
      # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
      # https://huggingface.co/docs/datasets/loading_datasets.html.
  
      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
      config_kwargs = {
          "cache_dir": model_args.cache_dir,
          "revision": model_args.model_revision,
          "use_auth_token": True if model_args.use_auth_token else None,
      }
      if model_args.config_name:
          config = AutoConfig.from_pretrained(
              model_args.config_name, **config_kwargs
          )
      elif model_args.model_name_or_path:
          config = AutoConfig.from_pretrained(
              model_args.model_name_or_path, **config_kwargs
          )
      else:
          config = CONFIG_MAPPING[model_args.model_type]()
          logger.warning(
              "You are instantiating a new config instance from scratch."
          )
          if model_args.config_overrides is not None:
              logger.info(
                  f"Overriding config: {model_args.config_overrides}"
              )
              config.update_from_string(model_args.config_overrides)
              logger.info(f"New config: {config}")
  
      tokenizer_kwargs = {
          "cache_dir": model_args.cache_dir,
          "use_fast": model_args.use_fast_tokenizer,
          "revision": model_args.model_revision,
          "use_auth_token": True if model_args.use_auth_token else None,
      }
      if model_args.tokenizer_name:
          tokenizer = AutoTokenizer.from_pretrained(
              model_args.tokenizer_name, **tokenizer_kwargs
          )
      elif model_args.model_name_or_path:
          tokenizer = AutoTokenizer.from_pretrained(
              model_args.model_name_or_path, **tokenizer_kwargs
          )
      else:
          raise ValueError(
              "You are instantiating a new tokenizer from scratch. This is not supported by this script."
              "You can do it from another script, save it, and load it from here, using --tokenizer_name."
          )
  
      if model_args.model_name_or_path:
          model = AutoModelForMaskedLM.from_pretrained(
              model_args.model_name_or_path,
              from_tf=bool(".ckpt" in model_args.model_name_or_path),
              config=config,
              cache_dir=model_args.cache_dir,
              revision=model_args.model_revision,
              use_auth_token=True
              if model_args.use_auth_token
              else None,
          )
      else:
          logger.info("Training new model from scratch")
          model = AutoModelForMaskedLM.from_config(config)
  
      model.resize_token_embeddings(len(tokenizer))
  
      # Preprocessing the datasets.
      # First we tokenize all the texts.
      if training_args.do_train:
          column_names = datasets["train"].column_names
      else:
          column_names = datasets["validation"].column_names
      text_column_name = (
          "text" if "text" in column_names else column_names[0]
      )
  
      padding = "max_length" if data_args.pad_to_max_length else False
  
      def tokenize_function(examples):
          # Remove empty lines
          examples["text"] = [
              line
              for line in examples["text"]
              if len(line) > 0 and not line.isspace()
          ]
          return tokenizer(
              examples["text"],
              padding=padding,
              truncation=True,
              max_length=data_args.max_seq_length,
          )
  
      tokenized_datasets = datasets.map(
          tokenize_function,
          batched=True,
          num_proc=data_args.preprocessing_num_workers,
          remove_columns=[text_column_name],
          load_from_cache_file=not data_args.overwrite_cache,
      )
  
      # Add the chinese references if provided
      if data_args.train_ref_file is not None:
          tokenized_datasets["train"] = add_chinese_references(
              tokenized_datasets["train"], data_args.train_ref_file
          )
      if data_args.validation_ref_file is not None:
          tokenized_datasets["validation"] = add_chinese_references(
              tokenized_datasets["validation"],
              data_args.validation_ref_file,
          )
      # If we have ref files, need to avoid it removed by trainer
      has_ref = (
          data_args.train_ref_file or data_args.validation_ref_file
      )
      if has_ref:
          training_args.remove_unused_columns = False
  
      # Data collator
      # This one will take care of randomly masking the tokens.
      data_collator = DataCollatorForWholeWordMask(
          tokenizer=tokenizer, mlm_probability=data_args.mlm_probability
      )
  
      # Initialize our Trainer
      trainer = Trainer(
          model=model,
          args=training_args,
          train_dataset=tokenized_datasets["train"]
          if training_args.do_train
          else None,
          eval_dataset=tokenized_datasets["validation"]
          if training_args.do_eval
          else None,
          tokenizer=tokenizer,
          data_collator=data_collator,
      )
  
      # Training
      if training_args.do_train:
          if last_checkpoint is not None:
              checkpoint = last_checkpoint
          elif (
              model_args.model_name_or_path is not None
              and os.path.isdir(model_args.model_name_or_path)
          ):
              checkpoint = model_args.model_name_or_path
          else:
              checkpoint = None
          train_result = trainer.train(
              resume_from_checkpoint=checkpoint
          )
          trainer.save_model()  # Saves the tokenizer too for easy upload
  
          output_train_file = os.path.join(
              training_args.output_dir, "train_results.txt"
          )
          if trainer.is_world_process_zero():
              with open(output_train_file, "w") as writer:
                  logger.info("***** Train results *****")
                  for key, value in sorted(
                      train_result.metrics.items()
                  ):
                      logger.info(f"  {key} = {value}")
                      writer.write(f"{key} = {value}\n")
  
              # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
              trainer.state.save_to_json(
                  os.path.join(
                      training_args.output_dir, "trainer_state.json"
                  )
              )
  
      # Evaluation
      results = {}
      if training_args.do_eval:
          logger.info("*** Evaluate ***")
  
          eval_output = trainer.evaluate()
  
          perplexity = math.exp(eval_output["eval_loss"])
          results["perplexity"] = perplexity
  
          output_eval_file = os.path.join(
              training_args.output_dir, "eval_results_mlm_wwm.txt"
          )
          if trainer.is_world_process_zero():
              with open(output_eval_file, "w") as writer:
                  logger.info("***** Eval results *****")
                  for key, value in sorted(results.items()):
                      logger.info(f"  {key} = {value}")
                      writer.write(f"{key} = {value}\n")
  
      return results
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/research_projects/mlm_wwm/run_mlm_wwm.py
imports: import json
  import logging
  import math
  import os
  import sys
  from dataclasses import dataclass, field
  from typing import Optional
  from datasets import Dataset, load_dataset
  import transformers
  from transformers import CONFIG_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, AutoConfig, AutoModelForMaskedLM, AutoTokenizer, DataCollatorForWholeWordMask, HfArgumentParser, Trainer, TrainingArguments, set_seed
  from transformers.trainer_utils import get_last_checkpoint, is_main_process
label_desc: Use image node_id 2 for calling a global function with example usage: main() and returns: results, tokenizer